There are a number of articulated and animated toys capable of interacting with human users in a way which appears intelligent such as those which are commercially available under the trademarks Furby® from Tiger Electronics, Ltd., and Barney® from MicroSoft Inc. These toys are capable of understanding speech, speaking in a natural language and demonstrating limited animation such as mouth, eye and ear movements. In addition, prior to the development of these more sophisticated toys, which generally include an embedded microprocessor and computer-based algorithm, other predecessors such as that which is commercially available under the trademark Teddy Ruxpin™ from YES! Entertainment Corporation, are also capable of exhibiting semi-intelligent behavior through speech and animation. Teddy Ruxpin™, and other toys like it, utilize a tape mechanism to provide the sound and animation control. Without exception, to date, a toy has never been developed which is capable of recognizing the human user who is playing with the toy. In addition, a toy has never been developed which is capable of recognizing inanimate objects with human-like faces such as dolls, stuffed animals or other toys.
There exists many methods for creating the semblance of intelligence in a toy or video game. Toys with animated moving parts are commonplace and anyone of ordinary skill in the art will be familiar with several methods to fabricate quasi-intelligent articulated toys. Similarly there exists many methods for the biometric identification of humans which includes fingerprint pattern matching, voice recognition, iris scanning, retina imaging as well as facial image recognition.
Fingerprint, iris and retina identification systems are considered “invasive”, expensive and not practical for applications where limited computer memory storage is available. Voice recognition, which is not the same as speech recognition, is somewhat less invasive, however it is cost prohibitive and can require excessive memory storage space for the various voice “templates”. In addition, identification processing delays can be excessive and unacceptable for many applications.
Face recognition is known and is perhaps the least invasive way to identify a human user. Another known advantage of a face recognition and identification system is that it can be constructed in such a way that its operation is transparent to the user. The prior art references are replete with biometric verification systems that have attempted to identify an individual based on a whole or partial digitized facial image. A major problem that has been recognized implicitly or explicitly by many prior reference inventors is that of securing adequate memory capacity for storing an encoded representation of a person's face on a medium that is compact and inexpensive. Because of this and other limitations, none of the prior references provides suitable means for use in articulated and animated toys. Notable among the prior reference patents pertaining to facial image recognition:
U.S. Pat. No. 3,805,238, wherein Rothfjell teaches an identification system in which major features (e.g. the shape of a person's nose in profile) are extracted from an image and stored. The stored features are subsequently retrieved and overlaid on a current image of the person to verify identity.
U.S. Pat. No. 4,712,103, wherein Gotanda teaches, inter alia, storing a digitized facial image in a non-volatile ROM on a key, and retrieving that image for comparison with a current image of the person at the time he/she request access to a secured area. Gotanda describes the use of image compression, by as much as a factor of four, to reduce the amount of data storage capacity needed by the ROM that is located on the key.
U.S. Pat. No. 4,858,000 wherein Lu teaches an image recognition system and method for identifying ones of a predetermined set of individuals, each of whom has a digital representation of his or her face stored in a defined memory space.
U.S. Pat. No. 4,975,969, wherein Tal teaches an image recognition system and method in which ratios of facial parameters (which Tal defines a distances between definable points on facial features such as a nose, mouth, eyebrow etc.) are measured from a facial image and are used to characterize the individual. Tal, like Lu in U.S. Pat. No. 4,858,000, uses a binary image to find facial features.
U.S. Pat. No. 5,031,228, wherein Lu teaches an image recognition system and method for identifying ones of a predetermined set of individuals, each of whom has a digital representation of his or her face stored in a defined memory space. Face identification data for each of the predetermined individuals are also stored in a Universal Face Model block that includes all the individual pattern images or face signatures stored within the individual face library.
U.S. Pat. No. 5,053,603, wherein Burt teaches an image recognition system using differences in facial features to distinguish one individual from another. Burt's system uniquely identifies individuals whose facial images and selected facial feature images have been learned by the system. Burt's system also “generically recognizes” humans and thus distinguishes between unknown humans and non-human objects by using a generic body shape template.
U.S. Pat. No. 5,164,992 wherein Turk and Pentland teach the use of an Eigenface methodology for recognizing and identifying members of a television viewing audience. The Turk et al system is designed to observe a group of people and identify each of the persons in the group to enable demographics to be incorporated in television ratings determinations.
U.S. Pat. No. 5,386,103, wherein Deban et al teach the use of an Eigenface methodology for encoding a reference face and storing said reference face on a card or the like, then retrieving said reference face and reconstructing it or automatically verifying it by comparing it to a second face acquired at the point of verification. Deban et al teach the use of this system in providing security for Automatic Teller Machine (ATM) transactions, check cashing, credit card security and secure facility access.
U.S. Pat. No. 5,432,864, wherein Lu et al teach the use of an Eigenface methodology for encoding a human facial image and storing it on an “escort memory” for later retrieval or automatic verification. Lu et al teach a method and apparatus for employing human facial image verification for financial transactions.
Although many inventors have offered approaches to providing an encoded facial image that could be stored, retrieved and compared, automatically or manually, at some later time for recognizing said human user, none have succeeded in producing a system that would be viable for use in an articulated and animated toy or video game. Part of the reason for this lies in the severe constraints imposed on the image storage aspect of a system by commercially available microprocessors. Another reason is that the complexity of the algorithms and the hardware necessary to implement them makes such a recognition system cost prohibitive for use with a toy.